Collaborative Embedding Training for Sparse User Data
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Solution Overview
Problem
Current recommendation systems face challenges in efficiently processing large amounts of digital content for relevance estimation due to computational expenses and sparse user interaction data, making it difficult to accurately recommend digital content to users.
Innovation Solution
The implementation of Transfer Learning (TL) techniques and Machine Learning Algorithms (MLAs) to generate predicted collaborative embeddings for digital items, leveraging content data even when user-item interaction data is limited, by training MLAs to adjust predicted embeddings to match target embeddings based on user interactions and content analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommendation systems process large amounts of digital content using conventional algorithms, then comprehensive content analysis is achieved, but computational costs and processing time increase significantly
Solution Approach 1:
The system performs preliminary embedding generation for digital items using content-based features before actual recommendation queries. Pre-computed embeddings are stored and reused, avoiding repeated expensive computations. This preliminary action reduces real-time processing costs while maintaining recommendation accuracy.
Solution Approach 2:
The patent introduces embedding vectors as an intermediary representation between raw digital content and recommendation algorithms. These embeddings capture semantic meaning in a compressed form, enabling efficient similarity computations without processing full content, thus reducing computational costs while preserving recommendation quality.
2Reliability
If recommendation systems rely heavily on user-item interaction data, then personalized recommendations improve, but the system fails when user interaction data is sparse or unavailable
Solution Approach 1:
The patent segments the recommendation problem into two independent components: content-based embedding generation and interaction-based refinement. By separating these functions, the system can generate meaningful embeddings even without interaction data, then optionally enhance them with interaction patterns when available, thus improving reliability across different data conditions.
Solution Approach 2:
Content-based embeddings serve as an intermediary that bridges the gap when user interaction data is sparse. These embeddings provide a fallback mechanism that maintains recommendation functionality independently of interaction data, improving system reliability in data-sparse scenarios while still allowing interaction data to enhance personalization when present.
3Measurement precision
If the system processes and stores detailed information about all digital items, then recommendation quality improves, but storage requirements and system complexity increase
Solution Approach 1:
The system extracts essential semantic features from digital content and stores them as compact embedding vectors rather than storing complete content or detailed metadata. This extraction process retains the most important information for recommendation while dramatically reducing storage requirements and system complexity.
Solution Approach 2:
The patent transforms rich, high-dimensional content representations into lower-dimensional embedding vectors that capture essential semantic properties. This parameter transformation maintains sufficient information for accurate recommendations while reducing storage volume and computational complexity through dimensionality reduction.
Data Source
AI summary
Methods and servers for training a Machine Learning Algorithm (MLA) to generate predicted collaborative embeddings are disclosed. The method includes generating a training set for a training item which includes (i) a target collaborative embedding generated by an other MLA based on previous user-item interactions that are sufficient for generating the target collaborative embedding, (ii) the training item. During a given training iteration of the MLA, the server (i) inputs the training item into the MLA that generates a predicted collaborative embedding, (ii) determines a penalty score by comparing the predicted collaborative embedding generated by the MLA and the target collaborative embedding generated by the other MLA, and (iii) adjusts the MLA using the penalty score so as to increase the similarity between the predicted collaborative embedding and the target collaborative embedding of the training item.